RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning
Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks. Optimizing either alone can shift the bottleneck to the other. In MoE RL, rollout-time routing replay exposes every sample's sequence length and layer-wise expert demand before its training step. We present RoutePack, a hierarchical planner that coordinates state-consistent, layer-wise expert rerouting with joint attention- and expert-aware data packing over an optimizer-step window. RoutePack first places experts indepe...
| Authors | Yibo Shen, Xudong Han, Xiaowei Zhu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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